A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

Supply Chain Modeling and Green Supply Chain: Literature Revue

A green supply chain should be rethought towards the term greening, whereas greening concerns in particular the environment, a lot of research works has been carried out jointly on the supply chain and the environmental dimension, exclusively supply chain modeling. This article is intended to present, first of all a summarized literature review of supply chain, green supply chain, and its modeling. Many researchers have proposed different models of green supply chain, except that each model is specific to the studied supply chain. Tending to meet this challenge the contribution of this paper is to propose a general framework of the green supply chains modeling

Problematising the concept of 'sustainability' in the supply chain through systematic literature review

The authors contend that there are two broad 'senses' in which 'sustainability' is currently understood in supply chain research and practice – responsibility (in terms of environmental and social practices) and continuity (in the face of twenty-first century uncertainty and disruption). Systematic review is used to illustrate the predominance of the responsibility 'sense' of sustainability in academic literature labelled 'sustainable supply chain.' The authors propose that parallel research into strategies for supply chain continuity (e.g. agility and resilience) be brought within the fold of the 'sustainable supply chain' research label for the sake of clarity of the 'sustainability' concept and the development of a truly sustainable supply chain, because a responsible supply chain might not necessarily be a resilient supply chain in the twenty-first century global environment

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

A closed-loop supply chain network in the edible oil industry using a novel robust stochastic-possibilistic programming

In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry

On modeling the single period spare parts distribution system design problem by mixed integer linear optimization

Efficiency and effectiveness of spare parts logistics play a significant role in changing customers’ service levels. A company providing high quality after-sales support to their customers gains competitive advantages. To study a single period multi commodity spare parts distribution system design problem, we present a mathematical model in the form of a mixed integer linear programming problem formulation. The mathematical model incorporates facility location decisions and vehicle size selection as well as routing decisions. The problem formulation minimizes the total cost including opening and operating costs of the depots and transportation costs for the vehicles. In order to define and solve a realistic spare parts distribution system design problem, we use aggregation on the commodity flow data to reduce the size of the problem and generate the outbound distribution routes from the regional depots to the service points apriori to simplify the mathematical model. The main focus of this study is the apriori route generation; we aim to observe the impact of different route sets obtained by different heuristic methods. The solution quality and the computation time to solve the problems to optimality are used to compare the performance of the three routing heuristic

Towards Environmentally Sustainable and Cost-Effective Food Distribution in the U.S.

Distribution centers (DCs) and supermarkets have an important role in food sustainability, but no previous research has accounted for their environmental impact. The purpose of this research was to assess environmental sustainability of grocery, perishables, and general merchandise DCs; to estimate food storing and retailing impact; and to provide cost-effective strategies to reduce DCs’ environmental impacts. The importance and relevance of the research is threefold: improving sustainability of DCs, food storing, and food retailing. The main method used in this research was the life cycle assessment (LCA) method. An initial study calculated environmental impacts of the Wal-Mart Stores, Inc. DCs, which combined a building energy consumption simulation, a process modeling tool for conveyors, regional water consumption and scarcity, and an LCA model of DCs’ material and construction environmental impacts. Further research provided an in-depth analysis of refrigerated zones within DCs and supermarkets in the United States. The study represents an initial attempt at assessing the environmental impact of food storage and retailing. We developed a model for calculating environmental impact of food storing and retailing in different states. Drawing on the data about DCs’ energy consumption and the impact of climate change, a multi-objective optimization model including cost, non-renewable fossil energy use, and climate change was developed. The optimization model used on-site solar panels and off-site wind technologies to find cost-effective energy mixes, which will reduce environmental impacts and shift DCs from energy consumers to energy producers and net zero DCs. We found solutions to the Pareto-optimal zero energy DCs, which were achieved by installing roof solar panels and/or erecting wind turbines at nearby locations. A pairwise Monte Carlo analysis showed when the switch to renewable energy became superior in terms of reducing fossil energy use and environmental impact. The research has shown variation of environmental impacts by building type, size, state, and climate zone; has identified which food has the highest and lowest storage and retailing impacts; and has found a feasible option to increase solar and wind energy use in DCs. Supporting datasets for chapters 2, 3, and 4 are included in Appendices 1, 2, and 3, respectively